Overview

Dataset statistics

Number of variables44
Number of observations36293
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 MiB
Average record size in memory168.0 B

Variable types

Numeric17
Categorical27

Warnings

loan_amnt is highly correlated with installmentHigh correlation
int_rate is highly correlated with grade and 1 other fieldsHigh correlation
installment is highly correlated with loan_amntHigh correlation
grade is highly correlated with int_rate and 1 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
revol_util is highly correlated with fico_averageHigh correlation
total_acc is highly correlated with open_accHigh correlation
fico_average is highly correlated with int_rate and 2 other fieldsHigh correlation
loan_amnt is highly correlated with installmentHigh correlation
int_rate is highly correlated with grade and 1 other fieldsHigh correlation
installment is highly correlated with loan_amntHigh correlation
grade is highly correlated with int_rate and 1 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
revol_util is highly correlated with fico_averageHigh correlation
total_acc is highly correlated with open_accHigh correlation
fico_average is highly correlated with int_rate and 2 other fieldsHigh correlation
loan_amnt is highly correlated with installmentHigh correlation
int_rate is highly correlated with grade and 1 other fieldsHigh correlation
installment is highly correlated with loan_amntHigh correlation
grade is highly correlated with int_rate and 1 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
total_acc is highly correlated with open_accHigh correlation
fico_average is highly correlated with int_rate and 1 other fieldsHigh correlation
region_Pacific is highly correlated with region_SouthHigh correlation
region_Northeast is highly correlated with region_SouthHigh correlation
revol_util is highly correlated with revol_balHigh correlation
verification_status_Source Verified is highly correlated with verification_status_VerifiedHigh correlation
term is highly correlated with int_rateHigh correlation
int_rate is highly correlated with term and 2 other fieldsHigh correlation
loan_status is highly correlated with last_credit_pull_d_monthHigh correlation
purpose_debt_consolidation is highly correlated with purpose_credit_cardHigh correlation
grade is highly correlated with int_rate and 1 other fieldsHigh correlation
last_credit_pull_d_month is highly correlated with loan_statusHigh correlation
open_acc is highly correlated with total_accHigh correlation
installment is highly correlated with loan_amntHigh correlation
verification_status_Verified is highly correlated with verification_status_Source Verified and 1 other fieldsHigh correlation
purpose_credit_card is highly correlated with purpose_debt_consolidationHigh correlation
revol_bal is highly correlated with revol_utilHigh correlation
region_South is highly correlated with region_Pacific and 1 other fieldsHigh correlation
loan_amnt is highly correlated with installment and 1 other fieldsHigh correlation
total_acc is highly correlated with open_accHigh correlation
fico_average is highly correlated with int_rate and 1 other fieldsHigh correlation
df_index has unique values Unique
grade has 9374 (25.8%) zeros Zeros
emp_length has 3105 (8.6%) zeros Zeros
delinq_2yrs has 32338 (89.1%) zeros Zeros
inq_last_6mths has 17611 (48.5%) zeros Zeros
revol_bal has 880 (2.4%) zeros Zeros
revol_util has 887 (2.4%) zeros Zeros
earliest_cr_line_month has 2473 (6.8%) zeros Zeros
last_credit_pull_d_month has 1965 (5.4%) zeros Zeros

Reproduction

Analysis started2021-08-09 00:59:44.712773
Analysis finished2021-08-09 01:02:51.272521
Duration3 minutes and 6.56 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct36293
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20369.09889
Minimum0
Maximum39734
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:02:51.556384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2099.2
Q110659
median20618
Q330242
95-th percentile37836.4
Maximum39734
Range39734
Interquartile range (IQR)19583

Descriptive statistics

Standard deviation11405.49018
Coefficient of variation (CV)0.5599408321
Kurtosis-1.179397689
Mean20369.09889
Median Absolute Deviation (MAD)9792
Skewness-0.05433916836
Sum739255706
Variance130085206.3
MonotonicityStrictly increasing
2021-08-08T20:02:51.830219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
258941
 
< 0.1%
156611
 
< 0.1%
136121
 
< 0.1%
13221
 
< 0.1%
74651
 
< 0.1%
54161
 
< 0.1%
279431
 
< 0.1%
320371
 
< 0.1%
115671
 
< 0.1%
Other values (36283)36283
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
397341
< 0.1%
397331
< 0.1%
397321
< 0.1%
397311
< 0.1%
397301
< 0.1%
397271
< 0.1%
397261
< 0.1%
397251
< 0.1%
397241
< 0.1%
397231
< 0.1%

loan_amnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct846
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10615.8481
Minimum500
Maximum33425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:02:52.542779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15250
median9600
Q315000
95-th percentile25000
Maximum33425
Range32925
Interquartile range (IQR)9750

Descriptive statistics

Standard deviation6600.741195
Coefficient of variation (CV)0.6217818053
Kurtosis0.03553042153
Mean10615.8481
Median Absolute Deviation (MAD)4600
Skewness0.8294382842
Sum385280975
Variance43569784.32
MonotonicityNot monotonic
2021-08-08T20:02:52.822606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002685
 
7.4%
120002208
 
6.1%
50001906
 
5.3%
60001812
 
5.0%
150001775
 
4.9%
80001515
 
4.2%
200001478
 
4.1%
250001211
 
3.3%
40001084
 
3.0%
7000974
 
2.7%
Other values (836)19645
54.1%
ValueCountFrequency (%)
5004
 
< 0.1%
7251
 
< 0.1%
7501
 
< 0.1%
8001
 
< 0.1%
9002
 
< 0.1%
9501
 
< 0.1%
1000264
0.7%
10504
 
< 0.1%
10751
 
< 0.1%
11003
 
< 0.1%
ValueCountFrequency (%)
334252
 
< 0.1%
330005
 
< 0.1%
328751
 
< 0.1%
325251
 
< 0.1%
325002
 
< 0.1%
324002
 
< 0.1%
323503
 
< 0.1%
322501
 
< 0.1%
3200021
0.1%
318256
 
< 0.1%

term
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
36
27189 
60
9104 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters72586
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36
2nd row60
3rd row36
4th row36
5th row36

Common Values

ValueCountFrequency (%)
3627189
74.9%
609104
 
25.1%

Length

2021-08-08T20:02:53.332291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:02:53.481200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3627189
74.9%
609104
 
25.1%

Most occurring characters

ValueCountFrequency (%)
636293
50.0%
327189
37.5%
09104
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72586
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
636293
50.0%
327189
37.5%
09104
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common72586
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
636293
50.0%
327189
37.5%
09104
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII72586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
636293
50.0%
327189
37.5%
09104
 
12.5%

int_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct363
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.91459813
Minimum5.42
Maximum22.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:02:53.720063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.17
Q18.94
median11.78
Q314.35
95-th percentile18.25
Maximum22.94
Range17.52
Interquartile range (IQR)5.41

Descriptive statistics

Standard deviation3.636127727
Coefficient of variation (CV)0.3051825741
Kurtosis-0.4941361328
Mean11.91459813
Median Absolute Deviation (MAD)2.57
Skewness0.2616253431
Sum432416.51
Variance13.22142485
MonotonicityNot monotonic
2021-08-08T20:02:54.015873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.99891
 
2.5%
11.49771
 
2.1%
7.51736
 
2.0%
13.49730
 
2.0%
7.88687
 
1.9%
7.49614
 
1.7%
9.99562
 
1.5%
7.9525
 
1.4%
11.71517
 
1.4%
5.42514
 
1.4%
Other values (353)29746
82.0%
ValueCountFrequency (%)
5.42514
1.4%
5.79383
1.1%
5.99323
0.9%
618
 
< 0.1%
6.03403
1.1%
6.17229
0.6%
6.3952
 
0.1%
6.54297
0.8%
6.62367
1.0%
6.76157
 
0.4%
ValueCountFrequency (%)
22.942
 
< 0.1%
22.857
 
< 0.1%
22.749
 
< 0.1%
22.641
 
< 0.1%
22.4811
< 0.1%
22.3514
< 0.1%
22.1123
0.1%
22.0622
0.1%
21.822
 
< 0.1%
21.7425
0.1%

installment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14333
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309.9563985
Minimum15.69
Maximum948.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:02:54.399639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum15.69
5-th percentile71.2
Q1165.38
median274.45
Q3410.02
95-th percentile684.04
Maximum948.47
Range932.78
Interquartile range (IQR)244.64

Descriptive statistics

Standard deviation188.0442444
Coefficient of variation (CV)0.6066796663
Kurtosis0.4188843934
Mean309.9563985
Median Absolute Deviation (MAD)117.99
Skewness0.9113804374
Sum11249247.57
Variance35360.63785
MonotonicityNot monotonic
2021-08-08T20:02:54.674467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.1167
 
0.2%
311.0254
 
0.1%
180.9653
 
0.1%
150.846
 
0.1%
368.4545
 
0.1%
339.3142
 
0.1%
372.1241
 
0.1%
186.6141
 
0.1%
330.7640
 
0.1%
317.7239
 
0.1%
Other values (14323)35825
98.7%
ValueCountFrequency (%)
15.691
< 0.1%
16.251
< 0.1%
16.311
< 0.1%
16.471
< 0.1%
19.871
< 0.1%
20.221
< 0.1%
21.251
< 0.1%
21.741
< 0.1%
21.811
< 0.1%
21.991
< 0.1%
ValueCountFrequency (%)
948.471
 
< 0.1%
946.892
 
< 0.1%
945.941
 
< 0.1%
945.352
 
< 0.1%
943.31
 
< 0.1%
939.261
 
< 0.1%
938.715
< 0.1%
938.311
 
< 0.1%
935.831
 
< 0.1%
934.551
 
< 0.1%

grade
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.54219822
Minimum0
Maximum6
Zeros9374
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2021-08-08T20:02:54.943301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.359563724
Coefficient of variation (CV)0.8815752126
Kurtosis0.06126521134
Mean1.54219822
Median Absolute Deviation (MAD)1
Skewness0.7893241789
Sum55971
Variance1.848413519
MonotonicityNot monotonic
2021-08-08T20:02:55.159169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
111102
30.6%
09374
25.8%
27458
20.5%
34810
13.3%
42447
 
6.7%
5877
 
2.4%
6225
 
0.6%
ValueCountFrequency (%)
09374
25.8%
111102
30.6%
27458
20.5%
34810
13.3%
42447
 
6.7%
5877
 
2.4%
6225
 
0.6%
ValueCountFrequency (%)
6225
 
0.6%
5877
 
2.4%
42447
 
6.7%
34810
13.3%
27458
20.5%
111102
30.6%
09374
25.8%

emp_length
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.035020527
Minimum0
Maximum10
Zeros3105
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2021-08-08T20:02:55.404017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.227703059
Coefficient of variation (CV)0.7999223392
Kurtosis-0.8817151312
Mean4.035020527
Median Absolute Deviation (MAD)2
Skewness0.6181271588
Sum146443
Variance10.41806704
MonotonicityNot monotonic
2021-08-08T20:02:55.645869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
18014
22.1%
104376
12.1%
24181
11.5%
33905
10.8%
43266
9.0%
53121
 
8.6%
03105
 
8.6%
62103
 
5.8%
71660
 
4.6%
81373
 
3.8%
ValueCountFrequency (%)
03105
 
8.6%
18014
22.1%
24181
11.5%
33905
10.8%
43266
9.0%
53121
 
8.6%
62103
 
5.8%
71660
 
4.6%
81373
 
3.8%
91189
 
3.3%
ValueCountFrequency (%)
104376
12.1%
91189
 
3.3%
81373
 
3.8%
71660
 
4.6%
62103
 
5.8%
53121
 
8.6%
43266
9.0%
33905
10.8%
24181
11.5%
18014
22.1%

annual_inc
Real number (ℝ≥0)

Distinct4770
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64296.72563
Minimum4000
Maximum262000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:02:55.932696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile24000
Q140000
median57000
Q380000
95-th percentile130000
Maximum262000
Range258000
Interquartile range (IQR)40000

Descriptive statistics

Standard deviation34193.39218
Coefficient of variation (CV)0.5318061199
Kurtosis3.700691557
Mean64296.72563
Median Absolute Deviation (MAD)18000
Skewness1.552907374
Sum2333521063
Variance1169188069
MonotonicityNot monotonic
2021-08-08T20:02:56.232510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600001437
 
4.0%
500001014
 
2.8%
40000845
 
2.3%
45000802
 
2.2%
30000780
 
2.1%
65000756
 
2.1%
75000754
 
2.1%
70000690
 
1.9%
48000689
 
1.9%
55000633
 
1.7%
Other values (4760)27893
76.9%
ValueCountFrequency (%)
40001
 
< 0.1%
40801
 
< 0.1%
48001
 
< 0.1%
50001
 
< 0.1%
55001
 
< 0.1%
60005
< 0.1%
70001
 
< 0.1%
72003
< 0.1%
75001
 
< 0.1%
78001
 
< 0.1%
ValueCountFrequency (%)
2620001
 
< 0.1%
2607351
 
< 0.1%
2600006
 
< 0.1%
2590001
 
< 0.1%
2580001
 
< 0.1%
2550001
 
< 0.1%
2520001
 
< 0.1%
25000028
0.1%
249999.961
 
< 0.1%
2499963
 
< 0.1%

loan_status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
1
31183 
0
5110 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
131183
85.9%
05110
 
14.1%

Length

2021-08-08T20:02:56.838136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:02:57.061998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
131183
85.9%
05110
 
14.1%

Most occurring characters

ValueCountFrequency (%)
131183
85.9%
05110
 
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
131183
85.9%
05110
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
131183
85.9%
05110
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131183
85.9%
05110
 
14.1%

dti
Real number (ℝ≥0)

Distinct2845
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.30177197
Minimum0
Maximum29.99
Zeros162
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:02:57.263873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.16
Q18.17
median13.39
Q318.57
95-th percentile23.82
Maximum29.99
Range29.99
Interquartile range (IQR)10.4

Descriptive statistics

Standard deviation6.66047643
Coefficient of variation (CV)0.5007209901
Kurtosis-0.851497575
Mean13.30177197
Median Absolute Deviation (MAD)5.2
Skewness-0.0257830506
Sum482761.21
Variance44.36194627
MonotonicityNot monotonic
2021-08-08T20:02:57.556695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0162
 
0.4%
1842
 
0.1%
1241
 
0.1%
13.238
 
0.1%
16.837
 
0.1%
12.4836
 
0.1%
19.235
 
0.1%
14.2934
 
0.1%
20.433
 
0.1%
21.633
 
0.1%
Other values (2835)35802
98.6%
ValueCountFrequency (%)
0162
0.4%
0.012
 
< 0.1%
0.025
 
< 0.1%
0.031
 
< 0.1%
0.043
 
< 0.1%
0.051
 
< 0.1%
0.061
 
< 0.1%
0.075
 
< 0.1%
0.085
 
< 0.1%
0.091
 
< 0.1%
ValueCountFrequency (%)
29.991
 
< 0.1%
29.951
 
< 0.1%
29.933
< 0.1%
29.922
< 0.1%
29.891
 
< 0.1%
29.881
 
< 0.1%
29.861
 
< 0.1%
29.851
 
< 0.1%
29.821
 
< 0.1%
29.791
 
< 0.1%

delinq_2yrs
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1474389001
Minimum0
Maximum11
Zeros32338
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:02:57.960452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4954341971
Coefficient of variation (CV)3.36026786
Kurtosis40.4189354
Mean0.1474389001
Median Absolute Deviation (MAD)0
Skewness5.074990062
Sum5351
Variance0.2454550437
MonotonicityNot monotonic
2021-08-08T20:02:58.262259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
032338
89.1%
13029
 
8.3%
2625
 
1.7%
3205
 
0.6%
457
 
0.2%
521
 
0.1%
610
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
032338
89.1%
13029
 
8.3%
2625
 
1.7%
3205
 
0.6%
457
 
0.2%
521
 
0.1%
610
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
91
 
< 0.1%
82
 
< 0.1%
74
 
< 0.1%
610
 
< 0.1%
521
 
0.1%
457
 
0.2%
3205
 
0.6%
2625
 
1.7%
13029
8.3%

inq_last_6mths
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8683492685
Minimum0
Maximum8
Zeros17611
Zeros (%)48.5%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:02:58.468135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.064322947
Coefficient of variation (CV)1.225685315
Kurtosis2.350606972
Mean0.8683492685
Median Absolute Deviation (MAD)1
Skewness1.35523876
Sum31515
Variance1.132783335
MonotonicityNot monotonic
2021-08-08T20:02:58.664012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
017611
48.5%
110044
27.7%
25321
 
14.7%
32807
 
7.7%
4285
 
0.8%
5134
 
0.4%
651
 
0.1%
728
 
0.1%
812
 
< 0.1%
ValueCountFrequency (%)
017611
48.5%
110044
27.7%
25321
 
14.7%
32807
 
7.7%
4285
 
0.8%
5134
 
0.4%
651
 
0.1%
728
 
0.1%
812
 
< 0.1%
ValueCountFrequency (%)
812
 
< 0.1%
728
 
0.1%
651
 
0.1%
5134
 
0.4%
4285
 
0.8%
32807
 
7.7%
25321
 
14.7%
110044
27.7%
017611
48.5%

open_acc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.201636679
Minimum2
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:02:58.904866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median9
Q312
95-th percentile17
Maximum42
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.343524842
Coefficient of variation (CV)0.4720382899
Kurtosis1.686392541
Mean9.201636679
Median Absolute Deviation (MAD)3
Skewness1.010533437
Sum333955
Variance18.86620806
MonotonicityNot monotonic
2021-08-08T20:02:59.146717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
73737
10.3%
63691
10.2%
83619
10.0%
93432
9.5%
52973
 
8.2%
102906
 
8.0%
112491
 
6.9%
42195
 
6.0%
122069
 
5.7%
131716
 
4.7%
Other values (29)7464
20.6%
ValueCountFrequency (%)
2536
 
1.5%
31393
 
3.8%
42195
6.0%
52973
8.2%
63691
10.2%
73737
10.3%
83619
10.0%
93432
9.5%
102906
8.0%
112491
6.9%
ValueCountFrequency (%)
421
 
< 0.1%
411
 
< 0.1%
391
 
< 0.1%
381
 
< 0.1%
362
 
< 0.1%
353
< 0.1%
345
< 0.1%
333
< 0.1%
322
 
< 0.1%
317
< 0.1%

pub_rec
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0.0
34349 
1.0
 
1888
2.0
 
46
3.0
 
8
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters108879
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.034349
94.6%
1.01888
 
5.2%
2.046
 
0.1%
3.08
 
< 0.1%
4.02
 
< 0.1%

Length

2021-08-08T20:02:59.723362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:02:59.880265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.034349
94.6%
1.01888
 
5.2%
2.046
 
0.1%
3.08
 
< 0.1%
4.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
070642
64.9%
.36293
33.3%
11888
 
1.7%
246
 
< 0.1%
38
 
< 0.1%
42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72586
66.7%
Other Punctuation36293
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
070642
97.3%
11888
 
2.6%
246
 
0.1%
38
 
< 0.1%
42
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.36293
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common108879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
070642
64.9%
.36293
33.3%
11888
 
1.7%
246
 
< 0.1%
38
 
< 0.1%
42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII108879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
070642
64.9%
.36293
33.3%
11888
 
1.7%
246
 
< 0.1%
38
 
< 0.1%
42
 
< 0.1%

revol_bal
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19911
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11507.32706
Minimum0
Maximum61126
Zeros880
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:03:00.118119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile320.6
Q13580
median8500
Q315964
95-th percentile33972.8
Maximum61126
Range61126
Interquartile range (IQR)12384

Descriptive statistics

Standard deviation10851.19799
Coefficient of variation (CV)0.9429816263
Kurtosis2.959222646
Mean11507.32706
Median Absolute Deviation (MAD)5686
Skewness1.616778891
Sum417635421
Variance117748497.7
MonotonicityNot monotonic
2021-08-08T20:03:00.373962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0880
 
2.4%
29814
 
< 0.1%
25514
 
< 0.1%
111
 
< 0.1%
68210
 
< 0.1%
11599
 
< 0.1%
399
 
< 0.1%
7989
 
< 0.1%
17639
 
< 0.1%
3469
 
< 0.1%
Other values (19901)35319
97.3%
ValueCountFrequency (%)
0880
2.4%
111
 
< 0.1%
25
 
< 0.1%
36
 
< 0.1%
43
 
< 0.1%
57
 
< 0.1%
68
 
< 0.1%
74
 
< 0.1%
85
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
611261
< 0.1%
610561
< 0.1%
610341
< 0.1%
610071
< 0.1%
609361
< 0.1%
609141
< 0.1%
609021
< 0.1%
607671
< 0.1%
607321
< 0.1%
607131
< 0.1%

revol_util
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1085
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.63212024
Minimum0
Maximum99.9
Zeros887
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:03:00.654787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7
Q125.3
median49
Q372.1
95-th percentile93.5
Maximum99.9
Range99.9
Interquartile range (IQR)46.8

Descriptive statistics

Standard deviation28.26541893
Coefficient of variation (CV)0.5812088551
Kurtosis-1.102439312
Mean48.63212024
Median Absolute Deviation (MAD)23.4
Skewness-0.02541723555
Sum1765005.54
Variance798.9339073
MonotonicityNot monotonic
2021-08-08T20:03:00.920626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0887
 
2.4%
0.262
 
0.2%
6360
 
0.2%
40.758
 
0.2%
66.756
 
0.2%
46.455
 
0.2%
0.154
 
0.1%
31.253
 
0.1%
6153
 
0.1%
27.252
 
0.1%
Other values (1075)34903
96.2%
ValueCountFrequency (%)
0887
2.4%
0.011
 
< 0.1%
0.031
 
< 0.1%
0.041
 
< 0.1%
0.051
 
< 0.1%
0.154
 
0.1%
0.121
 
< 0.1%
0.161
 
< 0.1%
0.262
 
0.2%
0.340
 
0.1%
ValueCountFrequency (%)
99.922
0.1%
99.821
0.1%
99.728
0.1%
99.620
0.1%
99.523
0.1%
99.421
0.1%
99.325
0.1%
99.216
< 0.1%
99.123
0.1%
9927
0.1%

total_acc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct81
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.72986526
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:03:01.194457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q113
median20
Q328
95-th percentile43
Maximum90
Range88
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.25061885
Coefficient of variation (CV)0.5177491307
Kurtosis0.764402394
Mean21.72986526
Median Absolute Deviation (MAD)7
Skewness0.8502515501
Sum788642
Variance126.5764245
MonotonicityNot monotonic
2021-08-08T20:03:01.428311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141373
 
3.8%
151370
 
3.8%
161365
 
3.8%
171345
 
3.7%
201331
 
3.7%
181315
 
3.6%
131310
 
3.6%
211297
 
3.6%
121256
 
3.5%
191247
 
3.4%
Other values (71)23084
63.6%
ValueCountFrequency (%)
23
 
< 0.1%
3169
 
0.5%
4394
 
1.1%
5520
1.4%
6646
1.8%
7786
2.2%
8957
2.6%
91021
2.8%
101121
3.1%
111200
3.3%
ValueCountFrequency (%)
901
< 0.1%
871
< 0.1%
811
< 0.1%
801
< 0.1%
791
< 0.1%
781
< 0.1%
761
< 0.1%
752
< 0.1%
741
< 0.1%
731
< 0.1%

fico_average
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean716.403604
Minimum632
Maximum827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.7 KiB
2021-08-08T20:03:01.684154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum632
5-th percentile667
Q1687
median712
Q3742
95-th percentile782
Maximum827
Range195
Interquartile range (IQR)55

Descriptive statistics

Standard deviation35.63689545
Coefficient of variation (CV)0.04974415993
Kurtosis-0.5454643541
Mean716.403604
Median Absolute Deviation (MAD)25
Skewness0.4745307574
Sum26000436
Variance1269.988317
MonotonicityNot monotonic
2021-08-08T20:03:01.913014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7021953
 
5.4%
6871943
 
5.4%
6821894
 
5.2%
6921893
 
5.2%
6971893
 
5.2%
7221695
 
4.7%
6771693
 
4.7%
7071682
 
4.6%
7271650
 
4.5%
7171631
 
4.5%
Other values (25)18366
50.6%
ValueCountFrequency (%)
6321
 
< 0.1%
6621323
3.6%
6671510
4.2%
6721555
4.3%
6771693
4.7%
6821894
5.2%
6871943
5.4%
6921893
5.2%
6971893
5.2%
7021953
5.4%
ValueCountFrequency (%)
8271
 
< 0.1%
82216
 
< 0.1%
81722
 
0.1%
812105
 
0.3%
807164
 
0.5%
802211
0.6%
797291
0.8%
792377
1.0%
787353
1.0%
782503
1.4%

earliest_cr_line_month
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.686110269
Minimum0
Maximum11
Zeros2473
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2021-08-08T20:03:02.171856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.510318308
Coefficient of variation (CV)0.6173496717
Kurtosis-1.306359247
Mean5.686110269
Median Absolute Deviation (MAD)3
Skewness-0.03099699287
Sum206366
Variance12.32233462
MonotonicityNot monotonic
2021-08-08T20:03:02.370733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
103761
10.4%
23715
10.2%
93632
10.0%
113297
9.1%
43163
8.7%
13001
8.3%
52790
7.7%
62685
7.4%
82646
7.3%
32605
7.2%
Other values (2)4998
13.8%
ValueCountFrequency (%)
02473
6.8%
13001
8.3%
23715
10.2%
32605
7.2%
43163
8.7%
52790
7.7%
62685
7.4%
72525
7.0%
82646
7.3%
93632
10.0%
ValueCountFrequency (%)
113297
9.1%
103761
10.4%
93632
10.0%
82646
7.3%
72525
7.0%
62685
7.4%
52790
7.7%
43163
8.7%
32605
7.2%
23715
10.2%

last_credit_pull_d_month
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.391590665
Minimum0
Maximum11
Zeros1965
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2021-08-08T20:03:02.586601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q311
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.853328491
Coefficient of variation (CV)0.5213124841
Kurtosis-1.146736968
Mean7.391590665
Median Absolute Deviation (MAD)2
Skewness-0.5768306573
Sum268263
Variance14.84814046
MonotonicityNot monotonic
2021-08-08T20:03:02.789475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1115027
41.4%
72416
 
6.7%
32320
 
6.4%
12220
 
6.1%
52097
 
5.8%
01965
 
5.4%
81919
 
5.3%
61811
 
5.0%
21752
 
4.8%
91627
 
4.5%
Other values (2)3139
 
8.6%
ValueCountFrequency (%)
01965
5.4%
12220
6.1%
21752
4.8%
32320
6.4%
41561
4.3%
52097
5.8%
61811
5.0%
72416
6.7%
81919
5.3%
91627
4.5%
ValueCountFrequency (%)
1115027
41.4%
101578
 
4.3%
91627
 
4.5%
81919
 
5.3%
72416
 
6.7%
61811
 
5.0%
52097
 
5.8%
41561
 
4.3%
32320
 
6.4%
21752
 
4.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
36292 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
036292
> 99.9%
11
 
< 0.1%

Length

2021-08-08T20:03:03.251192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:03.394104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
036292
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
036292
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
036292
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
036292
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
036292
> 99.9%
11
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
36199 
1
 
94

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
036199
99.7%
194
 
0.3%

Length

2021-08-08T20:03:03.774868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:03.917780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
036199
99.7%
194
 
0.3%

Most occurring characters

ValueCountFrequency (%)
036199
99.7%
194
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
036199
99.7%
194
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
036199
99.7%
194
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
036199
99.7%
194
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
33594 
1
 
2699

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
033594
92.6%
12699
 
7.4%

Length

2021-08-08T20:03:04.287552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:04.432465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
033594
92.6%
12699
 
7.4%

Most occurring characters

ValueCountFrequency (%)
033594
92.6%
12699
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
033594
92.6%
12699
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
033594
92.6%
12699
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
033594
92.6%
12699
 
7.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
18374 
1
17919 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
018374
50.6%
117919
49.4%

Length

2021-08-08T20:03:04.784249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:04.931158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
018374
50.6%
117919
49.4%

Most occurring characters

ValueCountFrequency (%)
018374
50.6%
117919
49.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018374
50.6%
117919
49.4%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018374
50.6%
117919
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018374
50.6%
117919
49.4%

verification_status_Source Verified
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
26967 
1
9326 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
026967
74.3%
19326
 
25.7%

Length

2021-08-08T20:03:05.320918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:05.464828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
026967
74.3%
19326
 
25.7%

Most occurring characters

ValueCountFrequency (%)
026967
74.3%
19326
 
25.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
026967
74.3%
19326
 
25.7%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
026967
74.3%
19326
 
25.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
026967
74.3%
19326
 
25.7%

verification_status_Verified
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
25361 
1
10932 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025361
69.9%
110932
30.1%

Length

2021-08-08T20:03:05.854590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:05.999500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
025361
69.9%
110932
30.1%

Most occurring characters

ValueCountFrequency (%)
025361
69.9%
110932
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025361
69.9%
110932
30.1%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025361
69.9%
110932
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025361
69.9%
110932
30.1%

purpose_credit_card
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
31612 
1
4681 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
031612
87.1%
14681
 
12.9%

Length

2021-08-08T20:03:06.372271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:06.531174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
031612
87.1%
14681
 
12.9%

Most occurring characters

ValueCountFrequency (%)
031612
87.1%
14681
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
031612
87.1%
14681
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
031612
87.1%
14681
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
031612
87.1%
14681
 
12.9%

purpose_debt_consolidation
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
19197 
1
17096 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019197
52.9%
117096
47.1%

Length

2021-08-08T20:03:06.883955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:07.024868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
019197
52.9%
117096
47.1%

Most occurring characters

ValueCountFrequency (%)
019197
52.9%
117096
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019197
52.9%
117096
47.1%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019197
52.9%
117096
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019197
52.9%
117096
47.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
35992 
1
 
301

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035992
99.2%
1301
 
0.8%

Length

2021-08-08T20:03:07.406636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:07.549547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
035992
99.2%
1301
 
0.8%

Most occurring characters

ValueCountFrequency (%)
035992
99.2%
1301
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
035992
99.2%
1301
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
035992
99.2%
1301
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
035992
99.2%
1301
 
0.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
33666 
1
 
2627

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
033666
92.8%
12627
 
7.2%

Length

2021-08-08T20:03:07.922317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:08.060231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
033666
92.8%
12627
 
7.2%

Most occurring characters

ValueCountFrequency (%)
033666
92.8%
12627
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
033666
92.8%
12627
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
033666
92.8%
12627
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
033666
92.8%
12627
 
7.2%

purpose_house
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
35957 
1
 
336

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035957
99.1%
1336
 
0.9%

Length

2021-08-08T20:03:08.459990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:08.613891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
035957
99.1%
1336
 
0.9%

Most occurring characters

ValueCountFrequency (%)
035957
99.1%
1336
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
035957
99.1%
1336
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
035957
99.1%
1336
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
035957
99.1%
1336
 
0.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
34244 
1
 
2049

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034244
94.4%
12049
 
5.6%

Length

2021-08-08T20:03:09.482357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:09.629268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
034244
94.4%
12049
 
5.6%

Most occurring characters

ValueCountFrequency (%)
034244
94.4%
12049
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
034244
94.4%
12049
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
034244
94.4%
12049
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
034244
94.4%
12049
 
5.6%

purpose_medical
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
35651 
1
 
642

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035651
98.2%
1642
 
1.8%

Length

2021-08-08T20:03:10.008033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:10.151947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
035651
98.2%
1642
 
1.8%

Most occurring characters

ValueCountFrequency (%)
035651
98.2%
1642
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
035651
98.2%
1642
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
035651
98.2%
1642
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
035651
98.2%
1642
 
1.8%

purpose_moving
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
35749 
1
 
544

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035749
98.5%
1544
 
1.5%

Length

2021-08-08T20:03:10.532712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:10.674626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
035749
98.5%
1544
 
1.5%

Most occurring characters

ValueCountFrequency (%)
035749
98.5%
1544
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
035749
98.5%
1544
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
035749
98.5%
1544
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
035749
98.5%
1544
 
1.5%

purpose_other
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
32660 
1
3633 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
032660
90.0%
13633
 
10.0%

Length

2021-08-08T20:03:11.041399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:11.183310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
032660
90.0%
13633
 
10.0%

Most occurring characters

ValueCountFrequency (%)
032660
90.0%
13633
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032660
90.0%
13633
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
032660
90.0%
13633
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
032660
90.0%
13633
 
10.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
36203 
1
 
90

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
036203
99.8%
190
 
0.2%

Length

2021-08-08T20:03:11.561078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:11.704991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
036203
99.8%
190
 
0.2%

Most occurring characters

ValueCountFrequency (%)
036203
99.8%
190
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
036203
99.8%
190
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
036203
99.8%
190
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
036203
99.8%
190
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
34696 
1
 
1597

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034696
95.6%
11597
 
4.4%

Length

2021-08-08T20:03:12.094750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:12.253654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
034696
95.6%
11597
 
4.4%

Most occurring characters

ValueCountFrequency (%)
034696
95.6%
11597
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
034696
95.6%
11597
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
034696
95.6%
11597
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
034696
95.6%
11597
 
4.4%

purpose_vacation
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
35949 
1
 
344

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035949
99.1%
1344
 
0.9%

Length

2021-08-08T20:03:12.719365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:12.902254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
035949
99.1%
1344
 
0.9%

Most occurring characters

ValueCountFrequency (%)
035949
99.1%
1344
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
035949
99.1%
1344
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
035949
99.1%
1344
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
035949
99.1%
1344
 
0.9%

purpose_wedding
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
35390 
1
 
903

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
035390
97.5%
1903
 
2.5%

Length

2021-08-08T20:03:13.361971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:13.506883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
035390
97.5%
1903
 
2.5%

Most occurring characters

ValueCountFrequency (%)
035390
97.5%
1903
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
035390
97.5%
1903
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
035390
97.5%
1903
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
035390
97.5%
1903
 
2.5%

region_Mountain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
33774 
1
 
2519

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
033774
93.1%
12519
 
6.9%

Length

2021-08-08T20:03:13.893643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:14.038556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
033774
93.1%
12519
 
6.9%

Most occurring characters

ValueCountFrequency (%)
033774
93.1%
12519
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
033774
93.1%
12519
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
033774
93.1%
12519
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
033774
93.1%
12519
 
6.9%

region_Northeast
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
27409 
1
8884 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
027409
75.5%
18884
 
24.5%

Length

2021-08-08T20:03:14.433311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:14.578222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
027409
75.5%
18884
 
24.5%

Most occurring characters

ValueCountFrequency (%)
027409
75.5%
18884
 
24.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
027409
75.5%
18884
 
24.5%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
027409
75.5%
18884
 
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
027409
75.5%
18884
 
24.5%

region_Pacific
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
28392 
1
7901 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
028392
78.2%
17901
 
21.8%

Length

2021-08-08T20:03:14.922011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:15.065922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
028392
78.2%
17901
 
21.8%

Most occurring characters

ValueCountFrequency (%)
028392
78.2%
17901
 
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028392
78.2%
17901
 
21.8%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028392
78.2%
17901
 
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028392
78.2%
17901
 
21.8%

region_Plains
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
35703 
1
 
590

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035703
98.4%
1590
 
1.6%

Length

2021-08-08T20:03:15.451686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:15.594597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
035703
98.4%
1590
 
1.6%

Most occurring characters

ValueCountFrequency (%)
035703
98.4%
1590
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
035703
98.4%
1590
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
035703
98.4%
1590
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
035703
98.4%
1590
 
1.6%

region_South
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.7 KiB
0
24662 
1
11631 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36293
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024662
68.0%
111631
32.0%

Length

2021-08-08T20:03:15.980362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T20:03:16.141269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
024662
68.0%
111631
32.0%

Most occurring characters

ValueCountFrequency (%)
024662
68.0%
111631
32.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36293
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024662
68.0%
111631
32.0%

Most occurring scripts

ValueCountFrequency (%)
Common36293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024662
68.0%
111631
32.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII36293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024662
68.0%
111631
32.0%

Interactions

2021-08-08T20:01:07.942467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:08.574368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:08.918158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:09.233963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:09.543772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:09.822601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:10.122416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:10.429228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:10.734041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:11.018868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:11.330673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:11.650479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:11.952291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:12.250108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:12.551923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:12.872726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:13.174541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:13.462363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:13.772172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:14.085979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:14.391792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:14.661625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:14.946449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:15.236271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:15.527094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:15.956827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:16.227661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:16.653400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:17.055154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:17.561840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:17.952715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:18.352497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:19.011628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:19.581289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:19.987029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:20.301837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:20.812522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:21.159307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:21.699976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:22.059756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:22.369564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:22.846271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:23.185062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:23.576820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:24.040536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:24.439291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:24.977959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:26.338123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:26.836815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:27.237569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:27.561371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:27.937140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:28.218964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:28.530774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:28.859574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:29.263324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:29.705053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:30.031849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:30.446594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:30.883325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:31.386016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:31.721809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:32.061600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:32.558296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:32.888094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:33.136940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:33.440752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:33.999410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:34.386172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:34.764939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:35.065267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:35.376074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:35.647910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:35.949722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:36.226551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:36.879150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:37.250920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:37.573734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:38.022448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:38.622077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:39.097785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:39.440574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:01:39.775367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-08-08T20:02:42.326078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:02:42.575930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:02:42.821835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:02:43.114678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-08T20:02:43.392517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-08-08T20:03:16.411741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-08T20:03:17.806662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-08T20:03:19.316733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-08T20:03:20.702878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-08T20:03:21.941119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-08T20:02:44.154989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-08T20:02:49.731792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexloan_amnttermint_rateinstallmentgradeemp_lengthannual_incloan_statusdtidelinq_2yrsinq_last_6mthsopen_accpub_recrevol_balrevol_utiltotal_accfico_averageearliest_cr_line_monthlast_credit_pull_d_monthhome_ownership_NONEhome_ownership_OTHERhome_ownership_OWNhome_ownership_RENTverification_status_Source Verifiedverification_status_Verifiedpurpose_credit_cardpurpose_debt_consolidationpurpose_educationalpurpose_home_improvementpurpose_housepurpose_major_purchasepurpose_medicalpurpose_movingpurpose_otherpurpose_renewable_energypurpose_small_businesspurpose_vacationpurpose_weddingregion_Mountainregion_Northeastregion_Pacificregion_Plainsregion_South
005000.03610.65162.871124000.0127.650.01.03.00.013648.083.79.0737.0411000101100000000000010000
112500.06015.2759.8321030000.001.000.05.03.00.01687.09.44.0742.0011000110000000000000000001
222400.03615.9684.332112252.018.720.02.02.00.02956.098.510.0737.0911000100000000000010000000
3310000.03613.49339.312149200.0120.000.01.010.00.05598.021.037.0692.030000110000000001000000100
455000.0367.90156.460336000.0111.200.03.09.00.07963.028.312.0732.094000110000000000000110000
567000.06015.96170.082847004.0123.510.01.07.00.017726.085.611.0692.0511000100010000000000000001
673000.03618.64109.434948000.015.350.02.04.00.08221.087.54.0662.042000110000000000000000100
785600.06021.28152.395440000.005.550.02.011.00.05210.032.613.0677.0011001010000000000010000100
895375.06012.69121.4511015000.0018.080.00.02.00.09279.036.53.0727.01111000101000000001000000001
9106500.06014.65153.452572000.0116.120.02.014.00.04032.020.623.0697.042001000010000000000010000

Last rows

df_indexloan_amnttermint_rateinstallmentgradeemp_lengthannual_incloan_statusdtidelinq_2yrsinq_last_6mthsopen_accpub_recrevol_balrevol_utiltotal_accfico_averageearliest_cr_line_monthlast_credit_pull_d_monthhome_ownership_NONEhome_ownership_OTHERhome_ownership_OWNhome_ownership_RENTverification_status_Source Verifiedverification_status_Verifiedpurpose_credit_cardpurpose_debt_consolidationpurpose_educationalpurpose_home_improvementpurpose_housepurpose_major_purchasepurpose_medicalpurpose_movingpurpose_otherpurpose_renewable_energypurpose_small_businesspurpose_vacationpurpose_weddingregion_Mountainregion_Northeastregion_Pacificregion_Plainsregion_South
362833972318500.03615.33644.305090000.015.240.00.08.00.022379.062.215.0662.0103000100100000000000001000
36284397244000.0367.75124.890061800.013.460.00.012.00.01918.017.116.0747.0111001000000000001000001000
362853972512750.03615.33444.055060000.0119.520.02.09.00.033074.068.89.0667.071000100100000000000001000
36286397265400.03612.17179.803035000.017.100.02.010.00.02040.036.221.0662.054000000010000000000001000
36287397273000.0368.7094.981562000.0112.230.00.010.00.03678.030.714.0737.034000000000100000000000001
36288397303000.0367.7593.670950000.015.350.00.017.00.021050.00.729.0762.0111001000000000000001000100
36289397313000.0367.7593.6701125000.012.140.00.015.00.021050.01.024.0762.0111001000000100000000000100
36290397324000.03610.91130.792018000.0118.000.01.04.00.05533.079.65.0707.016000100000000000000000001
36291397332000.0368.7063.3211070000.016.070.01.013.00.05967.019.817.0712.011000100100000000000001000
36292397344000.0367.43124.3101040000.013.450.00.02.00.0330.011.04.0772.095000100001000000000001000